Characteristics of transportation carbon emission center of gravity shift and spatial and temporal evolution in Western Region

Expand
  • School of Economics and Management, Southwest Forestry University, Kunming 650233, China

Received date: 2024-07-04

  Revised date: 2024-08-26

  Online published: 2024-10-17

Abstract

Grasping the spatial and temporal evolution of transportation carbon emissions in the western region is of great significance for promoting the high-quality development of transportation in the west and narrowing the gap between the east and the west. In this paper, on the basis of measuring the transportation carbon emissions of 11 provinces in the western region from 2013 to 2022 using the "top-down" method, the natural discontinuity method, standard deviation ellipse method and spatial correlation network method are used to reveal the evolution law from the three dimensions of regional non-equilibrium distribution characteristics, center of gravity transfer trajectory and spatial correlation. The study shows that: (1) The growth trend of transportation carbon emissions in the west is slow, and the overall unbalanced regional distribution is characterized by "high in the middle and low in the north and south"; (2) The center of gravity of transportation carbon emissions shifts from the northwest to the southwest, and the difference between the level of transportation in the area of center of gravity and the rest of the provinces increases due to the impacts of location, policy, and development, and the centripetal convergence force is enhanced; (3) There is a significant spatial correlation in the western transportation carbon emissions. There is a significant spatial correlation effect of transportation carbon emissions in the west, in which the overall correlation effect in the policy and economic drive there is an upward trend, the local correlation effect of the phenomenon of bifurcation, Northwest and Southwest China are respectively in the "low-low" and "high-high" aggregation area. The northwestern and southwestern regions are in the "low-low" and "high-high" agglomeration zones, respectively, and have formed a certain range of agglomeration effects with neighboring provinces.

Cite this article

GONG Haixiu, FU Wei .

Characteristics of transportation carbon emission center of gravity shift and spatial and temporal evolution in Western Region[J]. Science & Technology Review, 0 : 1 . DOI: 10.3981/j.issn.1000-7857.2024.07.00789

References

[1] 陈露露, 赵小风, 赖力. 江苏省交通运输业碳排放预测及减排情景分析研究[J]. 环境科学与管理, 2015, 40(10): 13-17.

[2] 曾晓莹, 邱荣祖, 林丹婷, 等. 中国交通碳排放及影响因素时空异质性[J]. 中国环境科学, 2020, 40(10): 4304-4313.

[3] 王杰, 郑琰, 姜晓红. 西南地区交通运输业碳排放测算与驱动因素分析[J]. 重庆理工大学学报(自然科学), 2023, 37(1): 249-256.

[4] 杨绍华, 张宇泉, 耿涌. 基于LMDI的长江经济带交通碳排放变化分析[J]. 中国环境科学, 2022, 42(10): 4817-4826.

[5] 孙彦明, 刘士显. “双碳” 目标下中国交通运输碳排放达峰预测[J]. 生态经济, 2023, 39(12): 33-40.

[6] 丁利杰, 朱泳丽. 中国交通运输业碳排放区域差异及脱钩效应[J]. 东南学术, 2023(4): 162-174.

[7] 刘俊豪, 刘榆欣, 黄蕾. 中国华东地区旅游交通碳排放测度与驱动因素分析[J]. 环境科学研究, 2024, 37(3): 626-636.

[8] 董会忠, 郭雪莲. 中国交通运输业碳排放效率时空演化特征研究[J]. 华东经济管理, 2023, 37(7): 70-80.

[9] 田佩宁, 毛保华, 童瑞咏, 等. 我国交通运输行业及不同运输方式的碳排放水平和强度分析[J]. 气候变化研究进展, 2023, 19(3): 347-356.

[10] 胡程, 丁正山, 穆学青, 等. 长江经济带旅游交通碳排放时空演变及驱动因素[J]. 南京师大学报(自然科学版), 2022, 45(1): 40-48.

[11] 喻洁, 达亚彬, 欧阳斌. 基于LMDI分解方法的中国交通运输行业碳排放变化分析[J]. 中国公路学报, 2015, 28(10): 112-119.

[12] 宋德勇, 宋沁颖, 张麒. 中国交通碳排放驱动因素分析: 基于脱钩理论与GFI分解法[J]. 科技管理研究, 2022, 42(11): 216-228.

[13] Tian Y H, Zhu Q H, Lai K H, et al. Analysis of greenhouse gas emissions of freight transport sector in China[J]. Journal of Transport Geography, 2014, 40: 43-52.

[14] 李建豹, 黄贤金, 揣小伟, 等. 长三角地区碳排放效率时空特征及影响因素分析[J]. 长江流域资源与环境, 2020, 29(7): 1486-1496.

[15] 蔺雪芹, 边宇, 王岱. 京津冀地区工业碳排放效率时空演化特征及影响因素[J]. 经济地理, 2021, 41(6): 187-195.

[16] 陈怡, 凌莉, 古圳威, 等. 陕西省碳排放时空格局演变及其影响因素[J]. 中国环境科学, 2024, 44(4): 1826-1839.

[17] 吕倩, 高俊莲. 京津冀地区交通运输碳排放模型及驱动因素分析[J]. 生态经济, 2018, 34(1): 31-36.

[18] 顾典. 中国交通运输业碳排放区域关联分析[J]. 上海海事大学学报, 2023, 44(3): 64-70, 118.

[19] Ang B W, Liu F L, Chung H S. A generalized Fisher index approach to energy decomposition analysis[J]. Energy Economics, 2004, 26(5): 757-763.

[20] 孙岩, 张昱, 刘学敏. 北京市交通碳排放的驱动因素分析: 基于城市发展视角[J]. 城市与环境研究, 2020, 7(1): 81-95.

[21] 王智琦, 李建国, 彭彬彬, 等. 西部地区交通碳排放的驱动因素与脱钩效应分析[J]. 环境工程, 2023, 41(10): 213-222.

[22] 李云燕, 张雪莹. 基于空间视角的交通运输规模对交通碳排放的影响路径[J/OL]. 环境科学, 1-20[2024-09-23]. https://doi.org/10.13227/j.hjkx.202311163.

[23] 田云, 张蕙杰. 中国农业碳排放效率时空格局及空间分异机理[J]. 社会科学辑刊, 2024(2): 172-182.

[24] 史琴琴, 鲁丰先, 陈海, 等. 中原经济区城镇居民消费间接碳排放时空格局及其影响因素[J]. 资源科学, 2018, 40(6): 1297-1306.

[25] Mi Z F, Meng J, Green F, et al. China’s “exported carbon” peak: Patterns, drivers, and implications[J]. Geophysical Research Letters, 2018, 45(9): 4309-4318.
Outlines

/